shapiq: Shapley Interactions for Machine Learning

Understand

Originally rooted in game theory, the Shapley Value (SV) has recently become an important tool in machine learning research.

• Perhaps most notably, it is used for feature attribution and data valuation in explainable artificial intelligence.
• Shapley Interactions (SIs) naturally extend the SV and address its limitations by assigning joint contributions to groups of entities, which enhance understanding of black box machine learning models.
• Due to the exponential complexity of computing SVs and SIs, various methods have been proposed that exploit structural assumptions or yield probabilistic estimates given limited resources.